


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


snake_case__				:	Any					       =					logging.getLogger(__name__)





class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						):




       '''simple docstring'''



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				str		,				__a				:				List[str]		,				__a				:				List[Any]=None		,				__a				:				Optional[int]=None							)					->Any:
          lowerCamelCase_						:   Optional[int]		= self.layer[current_layer](_lowerCamelCase		,				_lowerCamelCase		,				head_mask[current_layer]							)

          lowerCamelCase_						:   List[str]		= layer_outputs[0]

          return hidden_states







@add_start_docstrings(
    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."						,       __lowerCAmelCase						,       )
class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						):




       '''simple docstring'''



       def __init__(						self				:				List[Any]		,				__a				:				Union[str, Any]							)					->Union[str, Any]:
          super().__init__(_lowerCamelCase							)

          lowerCamelCase_						:   Tuple		= BertEncoderWithPabee(_lowerCamelCase							)

          self.init_weights()
          lowerCamelCase_						:   List[Any]		= 0
          lowerCamelCase_						:   int		= 0
          lowerCamelCase_						:   int		= 0

          lowerCamelCase_						:   List[Any]		= 0



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Dict							)					->Optional[Any]:
          lowerCamelCase_						:   Dict		= threshold



       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				List[str]							)					->List[str]:
          lowerCamelCase_						:   Any		= patience



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Dict:
          lowerCamelCase_						:   Dict		= 0
          lowerCamelCase_						:   Union[str, Any]		= 0



       def 				_lowerCAmelCase							(						self				:				Any							)					->int:
          lowerCamelCase_						:   str		= self.inference_layers_num / self.inference_instances_num
          lowerCamelCase_						:   Any		= (
              F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
              F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
          )
          print(_lowerCamelCase							)




       @add_start_docstrings_to_model_forward(_lowerCamelCase							)
       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Tuple=None		,				__a				:				Union[str, Any]=None		,				__a				:				Union[str, Any]=None		,				__a				:				List[str]=None		,				__a				:				str=None		,				__a				:				int=None		,				__a				:				Dict=None		,				__a				:				Union[str, Any]=None		,				__a				:				str=None		,				__a				:				List[str]=None		,				__a				:				List[Any]=False		,				)					->List[str]:

          if input_ids is not None and inputs_embeds is not None:
             raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""							)
          elif input_ids is not None:
             lowerCamelCase_						:   List[str]		= input_ids.size()
          elif inputs_embeds is not None:
             lowerCamelCase_						:   Any		= inputs_embeds.size()[:-1]
          else:
             raise ValueError("""You have to specify either input_ids or inputs_embeds"""							)

          lowerCamelCase_						:   Any		= input_ids.device if input_ids is not None else inputs_embeds.device

          if attention_mask is None:
             lowerCamelCase_						:   Any		= torch.ones(_lowerCamelCase		,				device=_lowerCamelCase							)
          if token_type_ids is None:
             lowerCamelCase_						:   Optional[Any]		= torch.zeros(_lowerCamelCase		,				dtype=torch.long		,				device=_lowerCamelCase							)

          # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
          # ourselves in which case we just need to make it broadcastable to all heads.
          lowerCamelCase_						:   List[str]		= self.get_extended_attention_mask(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase							)

          # If a 2D ou 3D attention mask is provided for the cross-attention
          # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
          if self.config.is_decoder and encoder_hidden_states is not None:
             lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Any		= encoder_hidden_states.size()
             lowerCamelCase_						:   int		= (encoder_batch_size, encoder_sequence_length)
             if encoder_attention_mask is None:
                lowerCamelCase_						:   str		= torch.ones(_lowerCamelCase		,				device=_lowerCamelCase							)
             lowerCamelCase_						:   Tuple		= self.invert_attention_mask(_lowerCamelCase							)
          else:
             lowerCamelCase_						:   Dict		= None

          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
          lowerCamelCase_						:   int		= self.get_head_mask(_lowerCamelCase		,				self.config.num_hidden_layers							)

          lowerCamelCase_						:   Any		= self.embeddings(
              input_ids=_lowerCamelCase		,				position_ids=_lowerCamelCase		,				token_type_ids=_lowerCamelCase		,				inputs_embeds=_lowerCamelCase							)
          lowerCamelCase_						:   List[str]		= embedding_output

          if self.training:
             lowerCamelCase_						:   List[Any]		= []
             for i in range(self.config.num_hidden_layers							):
                lowerCamelCase_						:   List[Any]		= self.encoder.adaptive_forward(
                    _lowerCamelCase		,				current_layer=_lowerCamelCase		,				attention_mask=_lowerCamelCase		,				head_mask=_lowerCamelCase							)

                lowerCamelCase_						:   List[Any]		= self.pooler(_lowerCamelCase							)
                lowerCamelCase_						:   Dict		= output_layers[i](output_dropout(_lowerCamelCase							)							)
                res.append(_lowerCamelCase							)
          elif self.patience == 0:  # Use all layers for inference
             lowerCamelCase_						:   str		= self.encoder(
                 _lowerCamelCase		,				attention_mask=_lowerCamelCase		,				head_mask=_lowerCamelCase		,				encoder_hidden_states=_lowerCamelCase		,				encoder_attention_mask=_lowerCamelCase		,				)
             lowerCamelCase_						:   List[str]		= self.pooler(encoder_outputs[0]							)
             lowerCamelCase_						:   Tuple		= [output_layers[self.config.num_hidden_layers - 1](_lowerCamelCase							)]
          else:
             lowerCamelCase_						:   Tuple		= 0
             lowerCamelCase_						:   int		= None
             lowerCamelCase_						:   List[str]		= 0
             for i in range(self.config.num_hidden_layers							):
                calculated_layer_num += 1
                lowerCamelCase_						:   List[Any]		= self.encoder.adaptive_forward(
                    _lowerCamelCase		,				current_layer=_lowerCamelCase		,				attention_mask=_lowerCamelCase		,				head_mask=_lowerCamelCase							)

                lowerCamelCase_						:   List[Any]		= self.pooler(_lowerCamelCase							)
                lowerCamelCase_						:   List[str]		= output_layers[i](_lowerCamelCase							)
                if regression:
                   lowerCamelCase_						:   List[Any]		= logits.detach()
                   if patient_result is not None:
                      lowerCamelCase_						:   List[str]		= patient_result.detach()
                   if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
                      patient_counter += 1
                   else:
                      lowerCamelCase_						:   List[str]		= 0
                else:
                   lowerCamelCase_						:   Union[str, Any]		= logits.detach().argmax(dim=1							)
                   if patient_result is not None:
                      lowerCamelCase_						:   int		= patient_result.detach().argmax(dim=1							)
                   if (patient_result is not None) and torch.all(labels.eq(_lowerCamelCase							)							):
                      patient_counter += 1
                   else:
                      lowerCamelCase_						:   Dict		= 0

                lowerCamelCase_						:   List[str]		= logits
                if patient_counter == self.patience:
                   break
             lowerCamelCase_						:   Optional[int]		= [patient_result]
             self.inference_layers_num += calculated_layer_num
             self.inference_instances_num += 1

          return res







@add_start_docstrings(
    "Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "						,       __lowerCAmelCase						,       )
class   SCREAMING_SNAKE_CASE_			(__lowerCAmelCase						):




       '''simple docstring'''



       def __init__(						self				:				Optional[Any]		,				__a				:				Union[str, Any]							)					->Any:
          super().__init__(_lowerCamelCase							)
          lowerCamelCase_						:   Optional[int]		= config.num_labels

          lowerCamelCase_						:   Tuple		= BertModelWithPabee(_lowerCamelCase							)
          lowerCamelCase_						:   List[str]		= nn.Dropout(config.hidden_dropout_prob							)
          lowerCamelCase_						:   Union[str, Any]		= nn.ModuleList(
              [nn.Linear(config.hidden_size		,				self.config.num_labels							) for _ in range(config.num_hidden_layers							)]							)

          self.init_weights()




       @add_start_docstrings_to_model_forward(_lowerCamelCase							)
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				List[Any]=None		,				__a				:				List[Any]=None		,				__a				:				str=None		,				__a				:				Optional[Any]=None		,				__a				:				Optional[Any]=None		,				__a				:				Dict=None		,				__a				:				Any=None		,				)					->int:
          lowerCamelCase_						:   Tuple		= self.bert(
              input_ids=_lowerCamelCase		,				attention_mask=_lowerCamelCase		,				token_type_ids=_lowerCamelCase		,				position_ids=_lowerCamelCase		,				head_mask=_lowerCamelCase		,				inputs_embeds=_lowerCamelCase		,				output_dropout=self.dropout		,				output_layers=self.classifiers		,				regression=self.num_labels == 1		,				)

          lowerCamelCase_						:   List[str]		= (logits[-1],)

          if labels is not None:
             lowerCamelCase_						:   Any		= None
             lowerCamelCase_						:   int		= 0
             for ix, logits_item in enumerate(_lowerCamelCase							):
                if self.num_labels == 1:
                   #  We are doing regression
                   lowerCamelCase_						:   Union[str, Any]		= MSELoss()
                   lowerCamelCase_						:   Tuple		= loss_fct(logits_item.view(-1							)		,				labels.view(-1							)							)
                else:
                   lowerCamelCase_						:   List[str]		= CrossEntropyLoss()
                   lowerCamelCase_						:   Any		= loss_fct(logits_item.view(-1		,				self.num_labels							)		,				labels.view(-1							)							)
                if total_loss is None:
                   lowerCamelCase_						:   Optional[Any]		= loss
                else:
                   total_loss += loss * (ix + 1)
                total_weights += ix + 1
             lowerCamelCase_						:   List[str]		= (total_loss / total_weights,) + outputs

          return outputs






"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   list[float]:




						'''simple docstring'''



						if radian_mode:
												return [magnitude * cos(__lowerCAmelCase							), magnitude * sin(__lowerCAmelCase							)]
						return [magnitude * cos(radians(__lowerCAmelCase							)							), magnitude * sin(radians(__lowerCAmelCase							)							)]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 10**-1							)				->   bool:




						'''simple docstring'''



						lowerCamelCase__   =cross(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =sum(__lowerCAmelCase							)
						return abs(__lowerCAmelCase							) < eps


if __name__ == "__main__":
	# Test to check if it works
	a				=array(
	    [
	        polar_force(718.4, 180 - 30),
	        polar_force(879.54, 45),
	        polar_force(100, -90),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem 1 in image_data/2D_problems.jpg
	a				=array(
	    [
	        polar_force(30 * 9.81, 15),
	        polar_force(215, 180 - 45),
	        polar_force(264, 90 - 30),
	    ]
	)

	a				=array([[0, 0], [0, 0], [0, 0]])

	assert in_static_equilibrium(forces, location)

	# Problem in image_data/2D_problems_1.jpg
	a				=array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

	a				=array([[0, 0], [6, 0], [10, 0], [12, 0]])

	assert in_static_equilibrium(forces, location)

	import doctest

	doctest.testmod()


